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Spectrum prediction is a key enabler of a range of emerging applications, from adaptive spectrum sensing to agile and proactive decision making, for dynamic spectrum access. Due to the fact that spectrum sensors easily experience an issue of missing readings and anomaly pollution, spectrum prediction with incomplete and corrupted historical observations has caused extensive concern. In this paper, we aim to tackle the challenging problem on how to accurately and efficiently recover the missing values from corrupted historical spectrum observations for approaching the limit of predictability in the spectrum prediction tasks. Specially, we first formulate a hankelized time-structured spectrum tensor that can naturally preserve both spectral and temporal dependencies among the historical spectrum observations. We then model the spectrum data recovery problem as tensor completion with its latent low-rank structure and sparse anomaly property. To efficiently solve the optimization problem, we design a robust online spectrum data recovery algorithm based on the alternating direction method. In addition, we also introduce the concept of maximum predictability to reveal the harmful effects of missing data and anomalies, as well as to evaluate the effectiveness of our proposed algorithm from an information theory perspective. Extensive experimental evaluations on the real-world spectrum observation dataset show that the proposed algorithm achieves significantly better spectrum data recovery performance in terms of both recovery accuracy and computation efficiency. It also indicates that not only the predictability of the frequency bands but also the prediction accuracy of any predictive algorithm can be improved by the proposed algorithm.

The rapid development of mobile communication services in recent years has resulted in a scarcity of spectrum resources. This paper addresses the problem of multi-dimensional resource allocation in cognitive radio systems. Deep reinforcement learning (DRL) combines deep learning and reinforcement learning to enable agents to solve complex problems. In this study, we propose a training approach based on DRL to design a strategy for secondary users in the communication system to share the spectrum and control their transmission power. The neural networks are constructed using the Deep Q-Network and Deep Recurrent Q-Network structures. The results of the conducted simulation experiments demonstrate that the proposed method can effectively improve the user’s reward and reduce collisions. In terms of reward, the proposed method outperforms opportunistic multichannel ALOHA by about 10% and about 30% for the single SU scenario and the multi-SU scenario, respectively. Furthermore, we explore the complexity of the algorithm and the influence of parameters in the DRL algorithm on the training.

A proactive spectrum monitoring and spectrum monitoring data (SMD) transmission coexistence system is investigated in dynamic spectrum sharing network, where a spectrum monitor (SM) aims to monitor the spectrum information from the electromagnetic signal sent by the suspicious transmitter to the suspicious destination by using a portion of its antennas and transmit its SMD to the spectrum data fusion center by using the rest antennas. The SMD transmission of the SM can also be employed to jam the suspicious destination to realise the effective proactive spectrum monitoring. Thus, the precoding and antenna selection scheme are jointly designed at the SM to maximise the sum of the achievable spectrum monitoring rate and the SMD transmission rate. The scenario that the SM can assess the statistical channel state information of the suspicious links based on the spectrum information obtained from the spectrum database as the spectrum management node is then considered. The spectrum monitoring/SMD transmission success probability is also derived, and a trade‐off between them is further revealed. Simulation results show that the proposed schemes can obtain higher sum rate of the spectrum monitoring and the SMD transmission, which also verifies its effectiveness and above "security‐reliability" trade‐off. [ABSTRACT FROM AUTHOR]

In cognitive radio networks (CRNs), dynamic spectrum access allows (unlicensed) users to identify and access unused channels opportunistically, thus improves spectrum utilization. In this paper, we address the user-channel allocation problem in multi-user multi-channel CRNs without a prior knowledge of channel statistics. The result of channel access is stochastic with unknown distribution, and statistically different for each user. In deciding the channel for access, a user needs to either explore a channel to learn its statistics, or exploit the channel with the highest expected reward based on the information collected so far. Further, a channel should be accessed exclusively by one user at a time to avoid collision. Using multi-armed bandit framework, we develop two rate-optimal algorithms with low computational complexities of $O(N)$ O (N) and $O(NK)$ O (N K) , respectively, where $N$ N denotes the number of users and $K$ K denotes the number of channels. Further, we extend the results and develop an algorithm that is amenable to implement in a distributed fashion. [ABSTRACT FROM AUTHOR]

Satellite-cellular convergence promises to enable higher millimetre-wave bandwidth (data rate); beamformed better signal alignment (higher system efficiency); multi-connectivity (higher data rates); and new use cases (verticals). Harnessing these opportunities will depend on overcoming challenges spanning shorter distance/reduced coverage and component complexity; construction of antenna array and over-the-air testing; coexistence issues between multiple mobile communication connections; performance tests; cybersecurity. This paper presents a broadband Monolithic Microwave Integrated Circuit (MMIC) Low-Noise Amplifier (LNA) based on a 0.15 µm gate length Gallium Arsenide (GaAs) pseudomorphic high electron transistor (pHEMT) technology for satellite-cellular convergence use cases applications. The designed three-stage 23-28 GHz LNA demonstrates an industry-leading flat gain response of 30 dB, a noise figure of 1.70 dB and a very low power dissipation of 43 mW. The differential sensitivity response spans 0.01 µs to 0.04 dBm/Hz over the upper and lower ends of the channel bandwidths of the 5G New Release frequency range n258 band (24.25-27.58 GHz). Moreover, the millimetre-wave regenerative sensitivity analysis of the designed LNA holds a grand promise for real-time component-level reconfiguration applications. These applications include dynamic spectrum access; regenerative wireless transponder-transceiver technologies support; active spectrum resource usage; distributed sensing over a multi-standards wideband spectrum; and massive and complex timevarying spectrum datasets/features. [ABSTRACT FROM AUTHOR]

Today, most spectrum allocation algorithms use conflict graphs to capture interference conditions. The use of conflict graphs, however, is often questioned by the wireless community for two reasons. First, building accurate conflict graphs requires significant overhead, and hence does not scale to outdoor networks. Second, conflict graphs cannot properly capture accumulative interference. In this paper, we use large-scale measurement data as ground truth to understand how severe these problems are and whether they can be overcome. We build "practical" conflict graphs using measurement-calibrated propagation models, which remove the need for exhaustive signal measurements by interpolating signal strengths using calibrated models. Calibrated models are imperfect, and we study the impact of their errors on multiple steps in the process, from calibrating propagation models, predicting signal strengths, to building conflict graphs. At each step, we analyze the introduction, propagation, and final impact of errors by comparing each intermediate result to its ground-truth counterpart. Our work produces several findings. Calibrated propagation models generate location-dependent prediction errors, ultimately producing conservative conflict graphs. While these "estimated conflict graphs" lower spectrum utilization, their conservative nature improves reliability by reducing the impact of accumulative interference. Finally, we propose a graph augmentation technique to address remaining accumulative interference.

This paper presents an algorithm that supports the dynamic spectrum access process in cognitive radio networks by generating a sorted list of best radio channels or by identifying those frequency ranges that are not in use temporarily. The concept is based on the reinforcement learning technique named Q-learning. To evaluate the utility of individual radio channels, spectrum monitoring is performed. In the presented solution, the epsilon-greedy action selection method is used to indicate which channel should be monitored next. The article includes a description of the proposed algorithm, scenarios, metrics, and simulation results showing the correct operation of the approach relied upon to evaluate the utility of radio channels and the epsilon-greedy action selection method. Based on the performed tests, it is possible to determine algorithm parameters that should be used in this proposed deployment. The paper also presents a comparison of the results with two other action selection methods

Future-generation wireless networks should accommodate surging growth in mobile data traffic and support an increasingly high density of wireless devices. Consequently, as the demand for spectrum continues to skyrocket, a severe shortage of spectrum resources for wireless networks will reach unprecedented levels of challenge in the near future. To deal with the emerging spectrum-shortage problem, dynamic spectrum access techniques have attracted a great deal of attention in both academia and industry. By exploiting the cognitive radio techniques, secondary users (SUs) are capable of accessing the underutilized spectrum holes of the primary users (PUs) to increase the whole system’s spectral efficiency with minimum interference violations. In this paper, we mathematically formulate the spectrum access problem for interweave cognitive radio networks, and propose a usage-aware deep reinforcement learning based scheme to solve it, which exploits the historical channel usage data to learn the time correlation and channel correlation of the PU channels. We evaluated the performance of the proposed approach by extensive simulations in both uncorrelated and correlated PU channel usage cases. The evaluation results validate the superiority of the proposed scheme in terms of channel access success probability and SU-PU interference probability, by comparing it with ideal results and existing methods.

Cognitive radio is a mechanism allowing dynamic access to spectrum channels. Since its beginnings, researchers have been working on using this inventive technology to control and manage the spectrum resources. Consequently, this research field has been progressing rapidly and important advances have been made. Spectrum sensing is a key function of cognitive radios that helps prevent the harmful interference with licensed users, as well as identifies the available spectrum to improve its utilization. Several spectrum sensing techniques are found in scientific literature. In this paper, we investigate the effect of the random sampling in spectrum sensing. We propose a spectrum sensing approach based on the energy detection and on the maximum eigenvalue detection (MED) combined with random sampling. The performance of the proposed approach is evaluated in terms of the receiver operating characteristics curves and in terms of the detection probability for different values of signal to noise ratio. The obtained results are compared to the uniform sampling case to show the added value of random sampling. [ABSTRACT FROM AUTHOR]

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This letter investigates a deep reinforcement learning (DRL)-based spectrum access scheme for device-to-device (D2D) communication underlay cellular networks. Specifically, cellular users (CUEs) and D2D pairs attempt to access the time slots (TSs) of a shared spectrum, and TSs are dynamically scheduled to CUEs in different frames. Based on the DRL theory, D2D pairs can be seen as a centralized agent which aims to learn an optimal spectrum access strategy to maximize the sum throughput without any prior information. In particular, with different locations of CUEs, the spectrum access manners for D2D communication are changed to ensure the communication quality of CUEs at the cell edge. Then, a double deep Q-network (DDQN) based D2D spectrum access (D4SA) algorithm is proposed, which makes D2D pairs learn to decide whether to access the spectrum in different TSs. Moreover, to ensure the fairness of resource allocation among D2D pairs, we improve the proposed algorithm and incorporate fairness into the objective function. Simulation results show that our proposed algorithm can achieve an optimal sum throughput close to the theoretical upper bound, where the performance is significantly improved compared to the scheme based on base station cooperation. [ABSTRACT FROM AUTHOR]

Wireless connectivity has become a significant part of human life all over the world, both in developing and developed countries. In order to provide sufficient coverage without the densification of cellular networks, relatively low carrier frequencies should be used. This paper considers the reuse of the digital terrestrial television (DTT) band for cellular system operation in Kenya, while protecting incumbent TV signal reception according to the Dynamic Spectrum Alliance (DSAL) rules. A state of the art model for DTT coverage and allowed cellular system power calculation is tested using real data for Kenya. Suggestions regarding future DSAL rules amendments are provided. Moreover, the amount of spectrum resources available for cellular system operation in the DTT band in Kenya is estimated against varying system parameters. [ABSTRACT FROM AUTHOR]

In vehicular ad hoc networks (VANETs), vehicles can communicate with other vehicles or devices through vehicle-to-X communication. However, with the rise of the Internet of Things, it is a real challenge to make the limited spectrum resource meet the increasing communication demands, i.e., the channel contention problem. To solve this problem, this paper proposes a strategy combining multi-hop forwarding through vehicles and dynamic spectrum access. Firstly, a group-based multi-hop broadcast protocol, G-hop, is proposed. G-hop classifies vehicles with similar characteristics such as moving speed and communication distance into same groups using the depth-first-search algorithm. Messages are forwarded within a group in priority and then across groups, which limits both the range and the number of relay vehicles, i.e., channel contenders. Further, we adopt deep reinforcement learning techniques to achieve dynamic spectrum access. We design a Global Optimization algorithm based on Experience Accumulation (GOEA) using deep reinforcement learning. In GOEA, a network structure combined with recurrent neural network and deep Q-network is proposed for learning the time-varying process, and then a reward method is applied to optimize the global utility. Vehicles that need to transmit messages select channels dynamically following the guidance of GOEA. The experimental results demonstrate that the G-hop protocol reduces the packet loss rate from 0.8 to about 0.1. Meanwhile, compared with Slotted Aloha and DQN, our GOEA algorithm reduces the collision probability and channel idle probability by 60%. Moreover, as the number of vehicles increases, the transmission success rate can be improved by 20%. [ABSTRACT FROM AUTHOR]

This paper presents an algorithm that supports the dynamic spectrum access process in cognitive radio networks by generating a sorted list of best radio channels or by identifying those frequency ranges that are not in use temporarily. The concept is based on the reinforcement learning technique named Q-learning. To evaluate the utility of individual radio channels, spectrum monitoring is performed. In the presented solution, the epsilon-greedy action selection method is used to indicate which channel should be monitored next. The article includes a description of the proposed algorithm, scenarios, metrics, and simulation results showing the correct operation of the approach relied upon to evaluate the utility of radio channels and the epsilon-greedy action selection method. Based on the performed tests, it is possible to determine algorithm parameters that should be used in this proposed deployment. The paper also presents a comparison of the results with two other action selection methods. [ABSTRACT FROM AUTHOR]

In cognitive radio networks models, quality of service (QoS) of primary users (PUs) must be assured. Dynamic spectrum access is a paradigm by which a radio system adjusts dynamically the use of convenient spectrum holes. In this paper, a secondary user reserved channel (SU-RC) model is proposed. SU-RC model introduces the use of a new SU reserved channel infrastructure to enhance QoS of SUs. Furthermore, SU-RC improves the efficiency of network by reducing the blocking probability and the forced termination probability of SUs. The proposed algorithm is significantly adaptable by deducing the optimal number of reservation channels. A reasonable balance between the success probability of channel selection and average number of channel switching is accomplished. Furthermore, this algorithm demonstrates the impact of PU's interference either behind or inside influenced region on SUs. Simulation results show that, by applying the SU-RC algorithm with a preferable number of reservation channels, the number of channel switching is still very close to that of the network without external SU-reserved channel. For example, for the case if SU is inside the PU's influenced region, when λ p = 20 , P f = 0.05 has constant the optimal number of reservation channels n opt = 2 for both cases either with or without the existence of reserved channel. Furthermore, since λ p = 10 , P m = 0.05 , the average number of channel switching S ¯ t is equal to 1.005 in case of without existence of the external reserved channel whereas S ¯ t is approximate 1.0275 which is regarded an in considerable increase of S ¯ t is about 0.0225. [ABSTRACT FROM AUTHOR]

To predict the dynamic response of high-speed trains (HSTs) passing through cable-stayed bridges (CSBs), this paper proposed a prediction framework based on the genetic algorithm (GA) and the neural network. Firstly, an HST-CSB coupled dynamic model was established using the rigid-flexible coupling method. Subsequently, a fused Temporal Convolutional Network and Long Short-Term Memory (TCN-LSTM) neural network model was developed, and its hyperparameters were optimized using the GA. The dataset was created based on the HST-CSB coupled dynamic model for training and testing the model. Additionally, the generalization capability of the model after adopting different track deformations was discussed. The main results were as follows: Comparative analysis with various neural network models revealed that the GA-TCN-LSTM model exhibited the most accurate prediction results across different train speeds. The significant temperature fluctuations of the bridge led to a drastic deterioration of the track smoothness, resulting in decreased predictive performance of the neural network models. The GA-TCN-LSTM model could optimize the hyperparameters, demonstrating good robustness and generalization capabilities. The predictive performance of the model improved when using the German low-interference spectrum, attributed to the greater irregularities in the German low-interference spectrum, resulting in larger train dynamic responses and stronger feature characteristics in the training data for the neural network. Different neural network models exhibited varying adaptability to track irregularities, with the GA-TCN-LSTM model demonstrating high accuracy in predicting dynamic responses under different train speeds. • A coupled dynamic model of high-speed train and cable-stayed bridges is established. • A model combining Temporal Convolutional Networks and Long Short-Term Memory network is established. • The genetic algorithm is employed to optimize the hyperparameters to mitigate the impact of them on model. • The generalization capability of the model under different conditions is discussed the performance. [ABSTRACT FROM AUTHOR]

Radio frequency interference (RFI) has been an enduring concern in radio astronomy, particularly for the observations of pulsars which require high timing precision and data sensitivity. In most works of the literature, RFI mitigation has been formulated as a detection task that consists of localizing possible RFI in dynamic spectra. This strategy inevitably leads to a potential loss of information since parts of the signal identified as possibly RFI-corrupted are generally not considered in the subsequent data processing pipeline. Conversely, this work proposes to tackle RFI mitigation as a joint detection and restoration that allows parts of the dynamic spectrum affected by RFI to be not only identified but also recovered. The proposed supervised method relies on a deep convolutional network whose architecture inherits the performance reached by a recent yet popular image-denoising network. To train this network, a whole simulation framework is built to generate large data sets according to physics-inspired and statistical models of the pulsar signals and of the RFI. The relevance of the proposed approach is quantitatively assessed by conducting extensive experiments. In particular, the results show that the restored dynamic spectra are sufficiently reliable to estimate pulsar times-of-arrivals with an accuracy close to the one that would be obtained from RFI-free signals. [ABSTRACT FROM AUTHOR]

Ref. [[1]] proposed a fast and practical algorithm for UAV relay networks to provide the optimal solution for the number of transmit time slots and the UAV relay location in a sequential manner. One of the key challenge of designing such a UAV-based network is a proper allocation of both the UAV's location and network resources. Due to the large-scale deployment of UAVs in UAV-based networks, also the shortage of spectrum is a realistic threat for the roll-out of these networks. [Extracted from the article]

Applications related to the Internet of Things are widely diversified and require both low latency and the access of massive sensors to wireless sensor networks. In physical wireless parameter conversion sensor networks (PhyC-SN), a fusion center (FC) can recognize information from all sensors on the frequency spectrum of the received signals via conversion from sensor information to signal frequency. The higher resolution of sensor information of PhyC-SNs requires securing more frequency bandwidth. Therefore, spectrum sharing between PhyC-SNs and other systems is essential. For this study, we assume that primary systems (PSs) refer to other wireless systems and that secondary systems (SSs) refer to the PhyC-SN. The SS detects any access from the PS and immediately stops access to an FC. This results in a loss of sensor information. Thus, the accuracy of the gathered sensor information by an FC is degraded. This paper proposes an adaptive channel assignment based on two predictions; sensor information and channel occupancy rate. In the proposed method, the predicted error caused by the cessation of channel access is calculated and the assignment is constructed by minimizing this predicted error. Since the sensor can select channels in accordance with the error of an instantaneous sensor result, the proposed channel assignment can utilize awareness of the instantaneous sensor result. The proposed method achieves high accuracy of gathered sensor information while delivering less frequent sensor information to the FC, thereby improving the utilization efficiency of frequency channels.

In a cognitive radio (CR) system, excessive access services for secondary users (SUs) lead to a substantial increase in congestion and the retrial phenomenon, both of which degrade the performance of CR networks, especially in overload conditions. This paper investigates the price-based spectrum access control policy that characterizes the network operator's provision to heterogeneous and delay-sensitive SUs through pricing strategies. Based on shared-use dynamic spectrum access (DSA), the SUs can occupy the dedicated spectrum without degrading the operations of primary users (PUs). The service to transmission of SUs can be interrupted by an arriving PU, while the interrupted SUs join a retrial pool called an orbit, later trying to use the spectrum to complete the service. In the retrial orbit, the interrupted SU competes fairly with other SUs in the orbit. Such a DSA mechanism is formulated as a retrial queue with service interruptions and general service times. Regarding the heterogeneity of delay-sensitive SUs, we consider two cases: the delay-sensitive parameter follows a discrete distribution and a continuous distribution, respectively. In equilibrium, we find that the revenue-optimal price is unique, while there may exist a continuum of equilibria for the socially optimal price. In addition, the socially optimal price is always not greater than the revenue-optimal price, and thus the socially optimal arrival rate is not less than the revenue-optimal one, which is contrary with the conclusion, i.e., the socially optimal and revenue-optimal arrival rates are consistent, drawn in the literature for homogeneous SUs. Finally, we present numerical examples to show the effect of various parameters on the operator's pricing strategies and SUs' behavior.

Dynamic spectrum access (DSA) emerging as an effective way of improving the utilization of the scarce spectrum has attracted great attention in the communication field. A key challenge in DSA is to perform an efficient spectrum sensing and sharing mechanism. In this paper, aiming at achieving a maximal spectrum utilization, we propose a privacy-aware crowdsourced spectrum sensing and multi-user sharing mechanism for DSA. Particularly, in the sensing stage, the advanced mobile crowd sensing is adapted to economically provide sufficient candidate sensing helpers for a sensing requestor. Considering the individual rationality and energy consumption, an incentive mechanism based on both monetary and social motivation is designed to motivate the finial participations of the sensing helpers. Moreover, with the increasing attention to individual privacy, a social network and location-based k-anonymity grouping algorithm are proposed to prevent each helper's privacy being attacked by the malicious requestor or mobile users. Then, for a sensing requestor, aiming at achieving a target detection performance with minimal payment, a truthful reverse auction-based winning group selection algorithm is designed. Furthermore, in the transmitting stage, a realistic scenario is considered where multiple transmitters may discover the same idle spectrum based on sensing helpers' detections and will transmit data simultaneously. Thus, we model this problem as a potential-game-based multi-user transmission mechanism where all the transmitters act as game players and will jointly adjust their transmission powers to maximize the global throughput. Accordingly, we also take advantage of an improved differential evolution algorithm for obtaining a better equilibrium solution in a decentralized way. Both the theoretical analysis and the simulation results prove the rationality and superiority of our proposed algorithms.

Next-generation mobile communication networks promise the support for vast number of IoT devices with strong demand for spectrum access. The cater for the continuous growth of IoT applications, one of the challenges to mobile communication network providers is to allow dynamic spectrum access to a gigantic number of densely distributed and low-power IoT networks. In this paper, a novel dynamic spectrum access method is proposed based on spectrum trading for distributed IoT devices. The notion of access benefit is introduced which is based on the purchased band number and modulation mode provided to the mobile user. In the proposed solution, IoT users aim achieve optimization of their spectrum access by ensuring that the spectrum price will not exceed the access benefits from the spectrum trading by solving the spectrum optimization function. Due to the complexity of optimization objective function, pattern search algorithm is utilized to complete the final spectrum allocation optimization. Numerical results are also provided to testify the performances of the proposed spectrum optimization method.

Traditional cognitive radio (CR) spectrum access techniques have been primitive and inefficient due to being blind to the occupancy conditions of the spectrum bands to be sensed. In addition, current spectrum access techniques are also unable to detect network changes or even consider the requirements of unlicensed users, leading to a poorer quality of service (QoS) and excessive latency. As user-specific approaches will play a key role in future wireless communication networks, the conventional CR spectrum access should also be updated in order to be more effective and agile. In this paper, a comprehensive and novel solution is proposed to decrease the sensing latency and to make the CR networks (CRNs) aware of unlicensed user requirements. As such, a proactive process with a novel QoS-based optimization phase is proposed, consisting of two different decision strategies. Initially, future traffic loads of the different radio access technologies (RATs), occupying different bands of the spectrum, are predicted using the artificial neural networks (ANNs). Based on these predictions, two strategies are proposed. In the first one, which solely focuses on latency, a virtual wideband (WB) sensing approach is developed, where predicted relative traffic loads in WB are exploited to enable narrowband (NB) sensing. The second one, based on $Q$ -learning, focuses not only on minimizing the sensing latency but also on satisfying other user requirements. The results reveal that the first strategy manages to significantly reduce the sensing latency of the random selection process by 59.6%, while the $Q$ -learning assisted second strategy enhanced the full-satisfaction by up to 95.7%.

We investigate the design of signal processing in wideband spectrum usage (SPU) measurements for efficient and smart dynamic spectrum access (DSA). In particular, we focus on spectrum usage detection (SPUD) in the experimental measurements. The detection results can be exploited to estimate statistics of the SPU. An appropriate design of the SPUD depends on the actual SPU in the target frequency band. There is a broad range of wireless systems in a considered broad measurement frequency band, such as from 60MHz to 6GHz, therefore a general design framework in the measurement frequency band is desired. In the proposed design framework, we at first define two models in terms of the SPU and the SPUD process. In addition, the proposed design procedure determines the adequate choice of parameters for the SPUD model based on given parameters of the SPU model in the target frequency band. Numerical evaluation based on computer simulations shows the validity of the design framework and design procedure. Moreover, a modified duty cycle (DC) estimation method is proposed, which can remove bias errors caused by low time resolution in the SPUD. Numerical evaluation based on experimental measurements demonstrates the practicality of the detection framework and procedure proposed in this work.

Current trends in spectrum regulation show that more and more unlicensed and shared spectrum bands are poised to be opened up for mobile communication. However, the question remains how to best utilize this spectrum and build efficient networks, and if the time has come for newer approaches to be considered for the next generation system. In this work, we propose a coordinated shared spectrum framework that can be considered for next generation cellular standardization. In designing the framework, we aim to improve upon the current unlicensed access schemes toward increasing spectral efficiency in highly-dense networks. To this end, we demonstrate that with the proposed framework both throughput and access delay can be significantly improved over the state-of-the-art LAA system. Also, by optimizing access delay and improving inter-operator resource fairness, the system is designed to be more amenable for operators to invest in deploying networks using shared spectrum. We further show that, by taking advantage of small timescale variations in traffic demand, large statistical multiplexing gains are possible through dynamic sharing instead of static, hard splitting of shared spectrum, as in the current CBRS system.

Power-domain based dynamic spectrum access (PDSA) techniques are proposed for sharing 28 GHz spectrum of any Mobile Network Operator (MNO) with in-building small cells (SCs) of the other countrywide. By controlling the transmission power of SCs, PDSA techniques explore the traditional interweave access by operating an SC at the maximum transmission power and the underlay access by allowing to operate an SC at a lowered transmission power separately, as well as jointly. Average capacity, spectral efficiency, energy efficiency, cost efficiency, and throughput per SC user equipment (UE) are derived for an arbitrary number of MNOs in a country. By varying the spectrum reuse factor for the millimeter-wave spectrum in each building of SCs, extensive numerical and simulation results and analyses for an illustrative scenario of a country consisting of four MNOs are carried out for the interweave and underlay techniques when applying separately, as well as the hybrid interweave-underlay technique and the static licensed spectrum allocation (SLSA) technique. It is shown that, due to gaining more shared spectra, the hybrid interweave-underlay technique provides the best, whereas the SLSA provides the worst, performances of all techniques in terms of the average capacity, spectral efficiency, energy efficiency, cost efficiency, and throughput per UE of an SC. Moreover, we show that the hybrid interweave-underlay technique, the interweave technique, and the underlay technique, respectively, can satisfy the expected requirements of spectral and energy efficiencies for Sixth-Generation (6G) networks by reusing each MNO's 28 GHz spectrum to SCs of about 33.33%, 50%, and 50% less number of buildings than that required by the SLSA for a spectrum reuse factor of six per building of small cells. [ABSTRACT FROM AUTHOR]

In wireless communication, there is an enormous increase in the number of users which results in spectrum shortage. This can be defeated through dynamic spectrum access scheme and cognitive radio is the best solution to achieve this. A cognitive radio (CR) is programmable, configurable and can dynamically use the spectrum in the wireless environment. One of the important tasks of CR is to perform spectrum sensing. Sensing done on spectrum facilitates the cognitive radio user to find the unoccupied area of the spectrum. This CR is prone to many attacks in the wireless environment like primary user emulation attack (PUEA), denial of service attack, replay attack, etc. Among them, the most important attack is PUEA. When a user mimics the primary user and acquires priority over other users to access the spectrum then it is termed as PUEA. In this work, PUEA detection and its defense techniques have been modeled to avoid the degradation of spectrum. To detect the malicious users feature detection-based sensing with double threshold has been proposed. If the detected signal falls above the upper threshold or below the lower threshold then it is considered as primary user signal, whereas if the signal falls in between the thresholds then that signal is identified as malicious user signal. The detection of PUEA cannot be accurate due to factors like false alarm and miss detection. Hence, a game model has been designed for the legitimate nodes to reach strategic defense decisions in the presence of multiple attackers. Based on the actions of the players, the defenders' payoff is determined to obtain optimal defense decisions. [ABSTRACT FROM AUTHOR]

Spectrum prediction is challenging owing to its complex inherent dependency and heterogeneity among the spectrum data. In this letter, we propose a novel end-to-end deep-learning-based model, entitled spatial-temporal-spectral prediction network (STS-PredNet), to collectively predict the states of various frequency bands in all locations of interest at the same time. More specifically, the predictive recurrent neural network (PredRNN) is trained to capture the spatial-temporal-spectral dependencies of spectrum data. Three components of PredRNN units are employed to model the three kinds of temporal properties in spectrum data, i.e. closeness, daily period, and weekly trend. The final prediction is then performed in a dynamically aggregated way. Extensive experiments are conducted based on a real-world spectrum measurement dataset, which illustrate the superiority of the proposed STS-PredNet over the state-of-the-art baselines. [ABSTRACT FROM AUTHOR]

Cognitive radio (CR) is a field which is gaining a lot of interest of researchers in around the whole world. It is also termed as nextgen technology as it aims to solve the problem of unutilized electromagnetic spectrum assigned to licensed users. With increasing wireless devices there is a demand of more spectrum resources and cognitive radio can help in making available the unused or underused spectrum for real time communication. When number of cognitive users communicate together at the same time there exists the problem of interference due to transmit power of all the devices to the licensed users. So there is a need of controlling power of transmission for cognitive users so that they may not interfere with Primary licensed users and maintain Quality of Service (QoS) with other cognitive users. The power control of a large number of devices together can be done by the application of computational intelligence techniques. In this paper three nature inspired computational intelligence techniques like Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Ant Colony Optimization (ABC) for optimization of transmit power in cognitive radio networks are discussed. [ABSTRACT FROM AUTHOR]

Cognitive radio network is enormously used to improve the spectrum utilization by enabling the dynamic spectrum access. The performance of the cognitive radio network is affected due to the denial of service caused by the primary user emulation attack. This primary user emulation attack restricts the spectrum access opportunity of secondary users. In this research, the integration of Secure Hash Algorithm -1 (SHA-1) and neural network (i.e., soft computing method) is proposed to avoid the primary user emulation attack. The Received Signal Strength (RSS) and Direction of Arrival (DoA) are used to achieve the localization between the primary and secondary users. The main objective of the proposed method is to minimize the loss ratio during the communication process. The performance of the RSS-DoA-SHA method is analyzed in terms of routing overhead, End-to-End Delay (EED) and loss ratio. The proposed RSS-DoA-SHA method is compared with two existing methods such as RSS-Angle of Arrival (AOA) method and Hybrid Routing Algorithm - Symmetric Key Cryptography (HRA-SKC) method to evaluate the performance measures. The end to end delay of the proposed method for 500 PU is 1ms, it is less when compared to the RSS-AOA method. [ABSTRACT FROM AUTHOR]